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This study aims to address the challenge of the real-time state of charge (SOC) estimation for lithium-ion batteries in robotic systems, which is critical for monitoring remaining battery power, planning task execution, conserving energy and extending battery lifespan.

The authors introduced an optimal observer based on adaptive dynamic programming for online SOC estimation, leveraging a second-order resistor–capacitor model for the battery. The model parameters were determined by fitting an exponential function to the voltage response from pulse current discharges, and the observer's effectiveness was verified through extensive experimentation.

The proposed optimal observer demonstrated significant improvements in SOC estimation accuracy, robustness and real-time performance, outperforming traditional methods by minimizing estimation errors and eliminating the need for iterative steps in the adaptive critic and actor updates.

This study contributes a novel approach to SOC estimation using an optimal observer that optimizes the observer design by minimizing estimation errors. This method enhances the robustness of SOC estimation against observation errors and uncertainties in battery behavior, representing a significant advancement in battery management technology for robotic applications.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

Autonomous underwater vehicle (AUV) is widely used in resource prospection and underwater detection due to its excellent performance. This study considers input saturation, nonlinear model uncertainties and external ocean current disturbances. The containment errors can be limited to a small neighborhood of zero in finite time by employing control strategy. The control strategy can keep errors within a certain range between the trajectory followed by AUVs and their intended targets. This can mitigate the issues of collisions and disruptions in communication which may arise from AUVs being in close proximity or excessively distant from each other.

The tracking errors are constrained. Based on the directed communication topology, a cooperative formation control algorithm for multi-AUV systems with constrained errors is designed. By using the saturation function, state observers are designed to estimate the AUV’s velocity in six degrees of freedom. A new virtual control algorithm is designed through combining backstepping technique and the tan-type barrier Lyapunov function. Neural networks are used to estimate and compensate for the nonlinear model uncertainties and external ocean current disturbances. A neural network adaptive law is designed.

The containment errors can be limited to a small neighborhood of zero in finite time so that follower AUVs can arrive at the convex hull consisting of leader AUVs within finite time. The validity of the results is indicated by simulations.

The state observers are designed to approximate the speed of the AUV and improve the accuracy of the control method. The anti-saturation function and neural network adaptive law are designed to deal with input saturation and general disturbances, respectively. It can ensure the safety and reliability of the multiple AUV systems.

This study addresses the lack of exploration of whether posting luxury products purchased on social media contributes to a favorable impression of oneself. It uncovers the underlying mechanisms shaping observers’ attitudes toward account holders sharing luxury posts. By examining the motive attribution processes triggered by such posts, particularly intrinsic and extrinsic motives, this study clarifies the dynamics of luxury posts on social media, addressing inconsistencies in the literature regarding responses to luxury brand users.

To test the hypotheses, a one-factor between-subjects experimental design involving 400 South Korean participants with social media accounts was employed. Participants in each condition recalled their experiences of encountering either luxury or non-luxury posts on social media and completed several measures, including attitudes toward the account holder.

The results indicate that luxury posts on social media positively influence attitudes toward account holders through intrinsic motive attribution but have a negative impact through extrinsic motive attribution. The study revealed that observers with lower materialistic values were more prone to infer the motives behind posting luxury products.

This study introduces motive attribution as a crucial mechanism for understanding the impact of luxury consumption on social media. Exploring the intrinsic and extrinsic motives inferred from luxury posts provides nuanced insights into how these posts shape observers’ evaluations of account holders. This study’s identification of distinct routes offers valuable perspectives for fashion marketing researchers and marketers.

Dysfunctional customer behavior (DCB) is costly and problematic for organizations. This research seeks to understand how DCB spreads and how businesses can effectively deal with it through employee intervention.

This research conducted a survey study and an experimental study to examine the proposed model.

Through two studies, we discovered that when an employee intervenes to stop DCB and is perceived as having high coping ability, observing customers learn from the employee’s action, resulting in reduced empathy toward the dysfunctional customer and diminished intentions to engage in DCB. Conversely, if they perceive the employee as having low coping ability, the intervention backfires, enhancing the observers’ empathy toward the dysfunctional customer and consequently leading them to engage in more DCB.

This research unveils an additional mechanism that explains the spread of DCB. It also contributes to the employee intervention literature by shedding light on when employee intervention can backfire. Further, our application of social learning theory along with the person-situation interaction literature offers a fresh perspective in explaining service exchanges.

This paper analyzes the ways in which accounting enables operations managers to enter and perform multiple roles in their interplay with organizational groups on the shop floor and in management, and the associated negotiations that operations managers have with “the self.”

Using field-based studies in a mining organization, the study draws on Goffman’s backstage–frontstage metaphor to analyze how operations managers enter and perform several roles with the aid of accounting.

The findings show that accounting legitimizes operations managers when they cross organizational boundaries, as accounting gives them an “entry ticket” that legitimizes their presence with the group. Accounting further allows operations managers to embrace more than one role by “putting on a mask” to become an outsider or insider in relation to a group. In performing their roles, operations managers exhibit varying attributes and knowledge. Accounting can thereby be withheld from, or shared with, organizational groups. The illusion of accounting as deterministic presented frontstage is not necessarily negotiated that way backstage. Rather, alternatives discussed backstage often become silenced in the frontstage performance. The study concludes that operations managers cross boundaries, embrace roles and exert agency as they navigate with accounting, enrolling it into their performance simultaneously as they backstage reflect upon accounting and its role for their everyday work.

This study relies on the frontstage/backstage metaphor to visualize the discrepancies in how accounting is enrolled into role performances and how seemingly categorical fronts do not necessarily share that dominant position backstage.

This study aims to promote the anti-disturbance and tracking accuracy performance of the servo systems, in which a modified active disturbance rejection control (MADRC) scheme is proposed.

An adaptive radial basis function (ARBF) neural network is utilized to estimate and compensate dominant friction torque disturbance, and a parallel high-gain extended state observer (PHESO) is employed to further compensate residual and other uncertain disturbances. This parallel compensation structure reduces the burden of single ESO and improves the response speed of permanent magnet synchronous motor (PMSM) to hybrid disturbances. Moreover, the sliding mode control (SMC) rate is introduced to design an adaptive update law of ARBF.

Simulation and experimental results show that as compared to conventional ADRC and SMC algorithms, the position tracking error is only 2.3% and the average estimation error of the total disturbances is only 1.4% in the proposed MADRC algorithm.

The disturbance parallel estimation structure proposed in MADRC algorithm is proved to significantly improve the performance of anti-disturbance and tracking accuracy.

This article proposes that survival can be a legitimate organizational goal, challenging a common view that dismisses it as unambitious or contrary to innovation. Drawing parallels from nature and survival strategies across various systems, it advocates that organizations, much like living organisms, should prioritize survivability (i.e. ability to survive) to ensure long-term success. Contrary to being seen as limiting, survival, when understood in its broad sense, can encompass and enhance performance goals such as growth. The article outlines the ERP factors —efficiency, resilience and prominence—as key to achieving survivability, offering a framework for organizations to manage resources, adapt to external forces and balance visibility to thrive amidst challenges.

Conceptual framework.

This model introduces the significance of survivability as an organizational goal.

This article argues for the consideration of survival as an overarching organizational goal, challenging the prevalent view that dismisses it as unambitious or contrary to innovation.

This study aims to improve the control accuracy and antidisturbance performance of the manipulator with the flexible link, a combined controller, which combines the novel backstepping sliding mode controller based on the extended state observer (ESO) and super-twisting sliding mode controller.

First, the dynamic of the system is constructed by Lagrange method and assumed mode method, and then the dynamic is decoupled by the singular perturbation theory to obtain the slow-varying subsystem and fast-varying subsystem. For the slow-varying subsystem, the novel backstepping sliding mode controller based on ESO is used to achieve joint tracking. For the fast-varying subsystem, the super-twisting sliding mode controller is used for vibration suppression. At the same time, to suppress chattering, the tanh function is used to replace the sign function in the reaching law.

The simulation results show that the combined control has better trajectory tracking performance, antiinterference performance and vibration suppression performance than traditional sliding mode control (SMC).

A novel backstepping sliding mode controller based on ESO is designed to guarantee the performance of the tracking trajectory. The new controller improves the converge rate. A super-twisting sliding mode controller, which can stabilize the fast-varying subsystem, is used to suppress the vibration of flexible link.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.